





"""simple docstring"""





import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput

class 						lowercase   (   _UpperCAmelCase   ):





      def __init__(			self ,		lowercase ,		lowercase ,		lowercase					)						->	int:
            lowerCAmelCase    		=			dataset
            lowerCAmelCase    		=			process
            lowerCAmelCase    		=			params





      def __len__(			self					)						->	Optional[Any]:
            return len(self.dataset					)





      def __getitem__(			self ,		lowercase					)						->	Any:
            lowerCAmelCase    		=			self.dataset[i]
            lowerCAmelCase    		=			self.process(lowercase ,		**self.params					)
            return processed







class 						lowercase   (   _UpperCAmelCase   ):





      def __init__(			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase=None					)						->	List[str]:
            lowerCAmelCase    		=			loader
            lowerCAmelCase    		=			infer
            lowerCAmelCase    		=			params
            if loader_batch_size == 1:
                  # Let's spare some time by deactivating altogether
                  lowerCAmelCase    		=			None
            lowerCAmelCase    		=			loader_batch_size

            # Internal bookkeeping
            lowerCAmelCase    		=			None
            lowerCAmelCase    		=			None





      def __len__(			self					)						->	Union[str, Any]:
            return len(self.loader					)





      def __iter__(			self					)						->	List[str]:
            lowerCAmelCase    		=			iter(self.loader					)
            return self





      def        _snake_case     (			self					)						->	Optional[int]:
            if isinstance(self._loader_batch_data ,		torch.Tensor					):
                  # Batch data is simple tensor, just fetch the slice
                  lowerCAmelCase    		=			self._loader_batch_data[self._loader_batch_index]
            else:
                  # Batch data is assumed to be BaseModelOutput (or dict)
                  lowerCAmelCase    		=			{}
                  for k, element in self._loader_batch_data.items():
                        if isinstance(lowercase ,		lowercase					):
                              # Convert ModelOutput to tuple first
                              lowerCAmelCase    		=			element.to_tuple()
                              if isinstance(element[0] ,		torch.Tensor					):
                                    lowerCAmelCase    		=			tuple(el[self._loader_batch_index].unsqueeze(0					) for el in element					)
                              elif isinstance(element[0] ,		np.ndarray					):
                                    lowerCAmelCase    		=			tuple(np.expand_dims(el[self._loader_batch_index] ,		0					) for el in element					)
                              continue
                        if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(lowercase ,		lowercase					):
                              # Those are stored as lists of tensors so need specific unbatching.
                              if isinstance(element[0] ,		torch.Tensor					):
                                    lowerCAmelCase    		=			tuple(el[self._loader_batch_index].unsqueeze(0					) for el in element					)
                              elif isinstance(element[0] ,		np.ndarray					):
                                    lowerCAmelCase    		=			tuple(np.expand_dims(el[self._loader_batch_index] ,		0					) for el in element					)
                              continue
                        if element is None:
                              # This can happen for optional data that get passed around
                              lowerCAmelCase    		=			None
                        elif isinstance(element[self._loader_batch_index] ,		torch.Tensor					):
                              # Take correct batch data, but make it looked like batch_size=1
                              # For compatibility with other methods within transformers

                              lowerCAmelCase    		=			element[self._loader_batch_index].unsqueeze(0					)
                        elif isinstance(element[self._loader_batch_index] ,		np.ndarray					):
                              # Take correct batch data, but make it looked like batch_size=1
                              # For compatibility with other methods within transformers
                              lowerCAmelCase    		=			np.expand_dims(element[self._loader_batch_index] ,		0					)
                        else:
                              # This is typically a list, so no need to `unsqueeze`.
                              lowerCAmelCase    		=			element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
                  lowerCAmelCase    		=			self._loader_batch_data.__class__(lowercase					)
            self._loader_batch_index += 1
            return result





      def        _snake_case     (			self					)						->	Dict:
            if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
                  # We are currently unrolling a batch so we just need to return
                  # the current item within a batch
                  return self.loader_batch_item()

            # We're out of items within a batch
            lowerCAmelCase    		=			next(self.iterator					)
            lowerCAmelCase    		=			self.infer(lowercase ,		**self.params					)
            # We now have a batch of "inferred things".
            if self.loader_batch_size is not None:
                  # Try to infer the size of the batch
                  if isinstance(lowercase ,		torch.Tensor					):
                        lowerCAmelCase    		=			processed
                  else:
                        lowerCAmelCase    		=			list(processed.keys()					)[0]
                        lowerCAmelCase    		=			processed[key]
                  if isinstance(lowercase ,		lowercase					):
                        lowerCAmelCase    		=			len(lowercase					)
                  else:
                        lowerCAmelCase    		=			first_tensor.shape[0]
                  if 0 < observed_batch_size < self.loader_batch_size:
                        # could be last batch so we can't unroll as many
                        # elements.
                        lowerCAmelCase    		=			observed_batch_size
                  # Setting internal index to unwrap the batch
                  lowerCAmelCase    		=			processed
                  lowerCAmelCase    		=			0
                  return self.loader_batch_item()
            else:
                  # We're not unrolling batches
                  return processed







class 						lowercase   (   _UpperCAmelCase   ):





      def __init__(			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase=None					)						->	Union[str, Any]:
            super().__init__(lowercase ,		lowercase ,		lowercase					)





      def __iter__(			self					)						->	Union[str, Any]:
            lowerCAmelCase    		=			iter(self.loader					)
            lowerCAmelCase    		=			None
            return self





      def        _snake_case     (			self					)						->	Optional[int]:
            if self.subiterator is None:
                  lowerCAmelCase    		=			self.infer(next(self.iterator					) ,		**self.params					)
            try:
                  # Try to return next item
                  lowerCAmelCase    		=			next(self.subiterator					)
            except StopIteration:
                  # When a preprocess iterator ends, we can start lookig at the next item
                  # ChunkIterator will keep feeding until ALL elements of iterator
                  # all have created their subiterator and have been iterating against.
                  #
                  # Another way to look at it, is we're basically flattening lists of lists
                  # into a single list, but with generators
                  lowerCAmelCase    		=			self.infer(next(self.iterator					) ,		**self.params					)
                  lowerCAmelCase    		=			next(self.subiterator					)
            return processed







class 						lowercase   (   _UpperCAmelCase   ):





      def __iter__(			self					)						->	List[str]:
            lowerCAmelCase    		=			iter(self.loader					)
            return self





      def        _snake_case     (			self					)						->	List[str]:
            # Extremely similar to PipelineIterator in its unpacking mechanism
            # BUT, we have an extra required item which is the presence of `is_last`
            # That is because everything is flattened by `PipelineChunkIterator` we
            # need to keep track of how to regroup here in the original `process`
            # boundaries so that `process` and `postprocess` see the same data.

            # This iterator accumulates items (possibly while unbatching) until it
            # its a `is_last` and then just passes it on to the caller.
            lowerCAmelCase    		=			False
            lowerCAmelCase    		=			[]
            if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
                  while self._loader_batch_index < self.loader_batch_size:
                        lowerCAmelCase    		=			self.loader_batch_item()
                        lowerCAmelCase    		=			item.pop("""is_last"""					)
                        accumulator.append(lowercase					)
                        if is_last:
                              return accumulator

            while not is_last:
                  lowerCAmelCase    		=			self.infer(next(self.iterator					) ,		**self.params					)
                  if self.loader_batch_size is not None:
                        if isinstance(lowercase ,		torch.Tensor					):
                              lowerCAmelCase    		=			processed
                        else:
                              lowerCAmelCase    		=			list(processed.keys()					)[0]
                              lowerCAmelCase    		=			processed[key]
                        if isinstance(lowercase ,		lowercase					):
                              lowerCAmelCase    		=			len(lowercase					)
                        else:
                              lowerCAmelCase    		=			first_tensor.shape[0]
                        if 0 < observed_batch_size < self.loader_batch_size:
                              # could be last batch so we can't unroll as many
                              # elements.
                              lowerCAmelCase    		=			observed_batch_size
                        lowerCAmelCase    		=			processed
                        lowerCAmelCase    		=			0
                        while self._loader_batch_index < self.loader_batch_size:
                              lowerCAmelCase    		=			self.loader_batch_item()
                              lowerCAmelCase    		=			item.pop("""is_last"""					)
                              accumulator.append(lowercase					)
                              if is_last:
                                    return accumulator
                  else:
                        lowerCAmelCase    		=			processed
                        lowerCAmelCase    		=			item.pop("""is_last"""					)
                        accumulator.append(lowercase					)
            return accumulator







class 						lowercase   (   _UpperCAmelCase   ):





      def __init__(			self ,		lowercase ,		lowercase					)						->	Tuple:
            lowerCAmelCase    		=			dataset
            lowerCAmelCase    		=			key





      def __len__(			self					)						->	Union[str, Any]:
            return len(self.dataset					)





      def __getitem__(			self ,		lowercase					)						->	Optional[int]:
            return self.dataset[i][self.key]







class 						lowercase   (   _UpperCAmelCase   ):





      def __init__(			self ,		lowercase ,		lowercase ,		lowercase					)						->	Optional[int]:
            lowerCAmelCase    		=			dataset
            lowerCAmelCase    		=			keya
            lowerCAmelCase    		=			keya





      def __len__(			self					)						->	str:
            return len(self.dataset					)





      def __getitem__(			self ,		lowercase					)						->	str:
            return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}













"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'speech_to_text_2'
      _SCREAMING_SNAKE_CASE							      = ['past_key_values']
      _SCREAMING_SNAKE_CASE							      = {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}





      def __init__(			self ,		lowercase=10_000 ,		lowercase=6 ,		lowercase=2_048 ,		lowercase=4 ,		lowercase=0.0 ,		lowercase=True ,		lowercase="relu" ,		lowercase=256 ,		lowercase=0.1 ,		lowercase=0.0 ,		lowercase=0.0 ,		lowercase=0.02 ,		lowercase=2 ,		lowercase=True ,		lowercase=1 ,		lowercase=0 ,		lowercase=2 ,		lowercase=1_024 ,		**lowercase ,		)						->	Optional[int]:
            lowerCAmelCase    		=			vocab_size
            lowerCAmelCase    		=			d_model
            lowerCAmelCase    		=			decoder_ffn_dim
            lowerCAmelCase    		=			decoder_layers
            lowerCAmelCase    		=			decoder_attention_heads
            lowerCAmelCase    		=			dropout
            lowerCAmelCase    		=			attention_dropout
            lowerCAmelCase    		=			activation_dropout
            lowerCAmelCase    		=			activation_function
            lowerCAmelCase    		=			init_std
            lowerCAmelCase    		=			decoder_layerdrop
            lowerCAmelCase    		=			use_cache
            lowerCAmelCase    		=			decoder_layers
            lowerCAmelCase    		=			scale_embedding  # scale factor will be sqrt(d_model) if True
            lowerCAmelCase    		=			max_target_positions

            super().__init__(
                pad_token_id=lowercase ,		bos_token_id=lowercase ,		eos_token_id=lowercase ,		decoder_start_token_id=lowercase ,		**lowercase ,		)






